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Analyzing Usb Device Connection History

CommunityPopular
mukul975
analyzing-usb-device-connection-history

Correlate Windows registry keys (USBSTOR, MountedDevices), Event Logs, and setupapi.dev.log to reconstruct USB device connection history, first/last-plugged timestamps, and drive letter mappings. Use when investigating removable media usage, tracking device provenance, or building a timeline for suspected data exfiltration.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameanalyzing-usb-device-connection-history
Stars
34.3K
Forks
4.2K
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Analyzing Usb Device Connection History AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-usb-device-connection-history
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-usb-device-connection-history .claude/skills/analyzing-usb-device-connection-history
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Usb Device Connection History in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Usb Device Connection History on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Usb Device Connection History is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analyzing USB Device Connection History

When to Use

  • When investigating potential data exfiltration via removable storage devices
  • During insider threat investigations to track USB device usage
  • For compliance audits verifying removable media policy enforcement
  • When correlating USB connections with file access and copy events
  • For establishing a timeline of device connections during an incident

Prerequisites

  • Forensic image or extracted registry hives and event logs
  • Access to SYSTEM, SOFTWARE, and NTUSER.DAT registry hives
  • SetupAPI logs (setupapi.dev.log)
  • Windows Event Logs (System, Security, DriverFrameworks-UserMode)
  • USBDeview, USB Forensic Tracker, or RegRipper
  • Understanding of USB device identification (VID, PID, serial number)

Workflow

Step 1: Extract USB-Related Artifacts

bash
# Mount forensic image and copy relevant artifacts
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence

mkdir -p /cases/case-2024-001/usb/

# Registry hives
cp /mnt/evidence/Windows/System32/config/SYSTEM /cases/case-2024-001/usb/
cp /mnt/evidence/Windows/System32/config/SOFTWARE /cases/case-2024-001/usb/
cp /mnt/evidence/Users/*/NTUSER.DAT /cases/case-2024-001/usb/

# SetupAPI logs (first connection timestamps)
cp /mnt/evidence/Windows/INF/setupapi.dev.log /cases/case-2024-001/usb/

# Event logs
cp /mnt/evidence/Windows/System32/winevt/Logs/System.evtx /cases/case-2024-001/usb/
cp "/mnt/evidence/Windows/System32/winevt/Logs/Microsoft-Windows-DriverFrameworks-UserMode%4Operational.evtx" \
   /cases/case-2024-001/usb/ 2>/dev/null
cp "/mnt/evidence/Windows/System32/winevt/Logs/Microsoft-Windows-Partition%4Diagnostic.evtx" \
   /cases/case-2024-001/usb/ 2>/dev/null

Step 2: Parse USBSTOR Registry Key

bash
# Extract USBSTOR entries from SYSTEM hive
python3 << 'PYEOF'
from Registry import Registry
import json

reg = Registry.Registry("/cases/case-2024-001/usb/SYSTEM")

# Find current ControlSet
select = reg.open("Select")
current = select.value("Current").value()
controlset = f"ControlSet{current:03d}"

# Parse USBSTOR
usbstor_path = f"{controlset}\\Enum\\USBSTOR"
usbstor = reg.open(usbstor_path)

devices = []
print("=== USBSTOR DEVICES ===\n")

for device_class in usbstor.subkeys():
    # Format: Disk&Ven_VENDOR&Prod_PRODUCT&Rev_REVISION
    class_name = device_class.name()
    parts = class_name.split('&')
    vendor = parts[1].replace('Ven_', '') if len(parts) > 1 else 'Unknown'
    product = parts[2].replace('Prod_', '') if len(parts) > 2 else 'Unknown'
    revision = parts[3].replace('Rev_', '') if len(parts) > 3 else 'Unknown'

    for instance in device_class.subkeys():
        serial = instance.name()
        last_write = instance.timestamp()

        device_info = {
            'vendor': vendor,
            'product': product,
            'revision': revision,
            'serial': serial,
            'last_connected': str(last_write),
        }

        # Get friendly name if available
        try:
            friendly = instance.value("FriendlyName").value()
            device_info['friendly_name'] = friendly
        except:
            pass

        # Get device parameters
        try:
            params = instance.subkey("Device Parameters")
            try:
                device_info['class_guid'] = params.value("ClassGUID").value()
            except:
                pass
        except:
            pass

        devices.append(device_info)
        print(f"Device: {vendor} {product}")
        print(f"  Serial: {serial}")
        print(f"  Last Connected: {last_write}")
        print(f"  Friendly Name: {device_info.get('friendly_name', 'N/A')}")
        print()

# Save results
with open('/cases/case-2024-001/analysis/usb_devices.json', 'w') as f:
    json.dump(devices, f, indent=2)

print(f"\nTotal USB storage devices found: {len(devices)}")
PYEOF

Step 3: Extract Drive Letter Assignments and User Associations

bash
# Parse MountedDevices from SYSTEM hive
python3 << 'PYEOF'
from Registry import Registry
import struct

reg = Registry.Registry("/cases/case-2024-001/usb/SYSTEM")

mounted = reg.open("MountedDevices")

print("=== MOUNTED DEVICES (Drive Letter Assignments) ===\n")
for value in mounted.values():
    name = value.name()
    data = value.value()

    if name.startswith("\\DosDevices\\"):
        drive_letter = name.replace("\\DosDevices\\", "")
        if len(data) > 24:
            # USB device - contains device path string
            try:
                device_path = data.decode('utf-16-le').strip('\x00')
                if 'USBSTOR' in device_path or 'USB#' in device_path:
                    print(f"  {drive_letter} -> {device_path}")
            except:
                pass
        else:
            # Fixed disk - contains disk signature + offset
            disk_sig = struct.unpack('<I', data[0:4])[0]
            offset = struct.unpack('<Q', data[4:12])[0]
            print(f"  {drive_letter} -> Disk Signature: 0x{disk_sig:08X}, Offset: {offset}")
PYEOF

# Parse user MountPoints2 (which user accessed which devices)
python3 << 'PYEOF'
from Registry import Registry
import os, glob

print("\n=== USER MOUNT POINTS (MountPoints2) ===\n")

for ntuser in glob.glob("/cases/case-2024-001/usb/NTUSER*.DAT"):
    try:
        reg = Registry.Registry(ntuser)
        mp2 = reg.open("Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\MountPoints2")

        print(f"User hive: {os.path.basename(ntuser)}")
        for key in mp2.subkeys():
            guid = key.name()
            last_write = key.timestamp()
            if '{' in guid:
                print(f"  Volume: {guid} | Last accessed: {last_write}")
        print()
    except Exception as e:
        print(f"  Error parsing {ntuser}: {e}")
PYEOF

Step 4: Extract First Connection Timestamps from SetupAPI

bash
# Parse setupapi.dev.log for USB device first-install timestamps
python3 << 'PYEOF'
import re

print("=== SETUPAPI USB DEVICE INSTALLATIONS ===\n")

with open('/cases/case-2024-001/usb/setupapi.dev.log', 'r', errors='ignore') as f:
    content = f.read()

# Find USB device installation sections
pattern = r'>>>\s+\[Device Install.*?\n.*?Section start (\d{4}/\d{2}/\d{2} \d{2}:\d{2}:\d{2}).*?\n(.*?)<<<'
matches = re.findall(pattern, content, re.DOTALL)

usb_installs = []
for timestamp, section in matches:
    if 'USBSTOR' in section or 'USB\\VID' in section:
        # Extract device ID
        dev_match = re.search(r'(USBSTOR\\[^\s]+|USB\\VID_\w+&PID_\w+[^\s]*)', section)
        if dev_match:
            device_id = dev_match.group(1)
            usb_installs.append({
                'first_install': timestamp,
                'device_id': device_id
            })
            print(f"  {timestamp} | {device_id}")

print(f"\nTotal USB installations found: {len(usb_installs)}")
PYEOF

# Parse Windows Event Logs for USB events
# Event IDs: 2003, 2010, 2100, 2102 (DriverFrameworks-UserMode)
# Event IDs: 6416 (Security - new external device recognized)
python3 << 'PYEOF'
import json
from evtx import PyEvtxParser

try:
    parser = PyEvtxParser("/cases/case-2024-001/usb/System.evtx")

    print("\n=== SYSTEM EVENT LOG USB EVENTS ===\n")
    for record in parser.records_json():
        data = json.loads(record['data'])
        event_id = str(data['Event']['System']['EventID'])

        # USB device connection events
        if event_id in ('20001', '20003', '10000', '10100'):
            timestamp = data['Event']['System']['TimeCreated']['#attributes']['SystemTime']
            event_data = data['Event'].get('UserData', data['Event'].get('EventData', {}))
            print(f"  [{timestamp}] EventID {event_id}: {json.dumps(event_data, default=str)[:200]}")
except Exception as e:
    print(f"Error: {e}")
PYEOF

Step 5: Build USB Activity Timeline and Report

bash
# Compile all USB evidence into a unified timeline
python3 << 'PYEOF'
import json, csv

timeline = []

# Load USBSTOR data
with open('/cases/case-2024-001/analysis/usb_devices.json') as f:
    devices = json.load(f)

for device in devices:
    timeline.append({
        'timestamp': device['last_connected'],
        'source': 'USBSTOR Registry',
        'device': f"{device['vendor']} {device['product']}",
        'serial': device['serial'],
        'event': 'Last Connected',
        'detail': device.get('friendly_name', '')
    })

# Sort chronologically
timeline.sort(key=lambda x: x['timestamp'])

# Write timeline CSV
with open('/cases/case-2024-001/analysis/usb_timeline.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['timestamp', 'source', 'device', 'serial', 'event', 'detail'])
    writer.writeheader()
    writer.writerows(timeline)

print(f"USB Timeline: {len(timeline)} events written to usb_timeline.csv")

# Print summary
print("\n=== USB DEVICE SUMMARY ===")
for entry in timeline:
    print(f"  {entry['timestamp']} | {entry['device']} | {entry['serial'][:20]} | {entry['event']}")
PYEOF

Key Concepts

ConceptDescription
USBSTORRegistry key storing USB mass storage device identification and connection data
VID/PIDVendor ID and Product ID uniquely identifying USB device manufacturer and model
Device serial numberUnique identifier for individual USB devices (some devices share serials)
MountedDevicesRegistry key mapping volume GUIDs and drive letters to physical devices
MountPoints2Per-user registry key showing which volumes a user accessed
SetupAPI logWindows driver installation log recording first-time device connections
DeviceContainersRegistry key in SOFTWARE hive with device metadata and timestamps
EMDMgmtRegistry key tracking ReadyBoost-compatible devices with serial numbers and timestamps

Tools & Systems

ToolPurpose
USB Forensic TrackerSpecialized tool for USB device history extraction
USBDeviewNirSoft tool listing all USB devices connected to a system
RegRipper (usbstor plugin)Automated USB artifact extraction from registry hives
Registry ExplorerInteractive registry analysis for USB-related keys
KAPEAutomated collection of USB-related artifacts
Plaso/log2timelineTimeline creation including USB connection events
FTK ImagerForensic imaging including removable media
VelociraptorEndpoint agent with USB device history hunting artifacts

Common Scenarios

Scenario 1: Data Exfiltration by Departing Employee Extract USBSTOR entries to identify all USB devices ever connected, correlate device serial numbers with MountPoints2 to confirm user access, cross-reference timestamps with file access logs and jump list recent files, check for large file copy patterns in USN journal.

Scenario 2: Unauthorized Device on Secure System Audit all USBSTOR entries against approved device list, identify unauthorized devices by VID/PID not matching corporate-approved hardware, determine when the unauthorized device was first and last connected, check if any data was transferred.

Scenario 3: Malware Delivery via USB Identify USB device connected just before malware execution (Prefetch timestamps), extract the device serial and vendor information, check if autorun was enabled for the device, look for executable launch from the removable drive letter in Prefetch and ShimCache.

Scenario 4: Tracking a Specific USB Drive Across Multiple Systems Search for the same device serial number in USBSTOR across all forensic images, build a map of which systems the drive was connected to and when, identify the chronological path of the device through the organization, correlate with network share access logs.

Output Format

USB Device History Analysis:
  System: DESKTOP-ABC123 (Windows 10 Pro)
  Total USB Storage Devices: 12
  Analysis Sources: USBSTOR, MountedDevices, MountPoints2, SetupAPI, Event Logs

  Device Inventory:
    1. Kingston DataTraveler 3.0 (Serial: 0019E06B4521A2B0)
       First Connected:  2024-01-10 09:15:32 (SetupAPI)
       Last Connected:   2024-01-18 14:30:00 (USBSTOR)
       Drive Letter:     E:
       User Access:      suspect_user (MountPoints2)

    2. WD My Passport (Serial: 575834314131363035)
       First Connected:  2024-01-15 20:00:00
       Last Connected:   2024-01-15 23:45:00
       Drive Letter:     F:
       User Access:      suspect_user

  Suspicious Findings:
    - Kingston drive connected 15 times during investigation period
    - WD Passport connected only once, late evening (unusual hours)
    - Unknown device (VID_1234&PID_5678) connected 2024-01-17, no matching approved device

  Timeline: /cases/case-2024-001/analysis/usb_timeline.csv

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing Usb Device Connection History AI skill do?

Correlate Windows registry keys (USBSTOR, MountedDevices), Event Logs, and setupapi.dev.log to reconstruct USB device connection history, first/last-plugged timestamps, and drive letter mappings. Use when investigating removable media usage, tracking device provenance, or building a timeline for suspected data exfiltration.

Why use Analyzing Usb Device Connection History on TypingMind?

Because you install it once and use it with any model. Analyzing Usb Device Connection History is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Usb Device Connection History in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-usb-device-connection-history. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Usb Device Connection History?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Usb Device Connection History?

As many as you like. As long as a model supports skills, you can use Analyzing Usb Device Connection History with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Usb Device Connection History AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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